用联邦学习生成结直肠癌病理图像，提升分类准确率。
Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology Images
- 将视觉Transformer与生成对抗网络结合生成病理图像。
- 在联邦学习框架下实现跨医院数据协作，分类准确率提升。
- 基于Kubernetes模拟多节点环境，适合医疗隐私保护场景。
深度学习中大型模型通常表现最佳，但需海量数据。组织学图像获取成本高且属敏感医疗信息，存在数据稀缺与隐私问题。视觉Transformer是当前计算机视觉的先进模型，在图像分类等任务中表现优异。本文结合视觉Transformer与生成对抗网络，生成与结直肠癌相关的病理图像，并通过扩充训练集提升分类准确率。随后，在真实Kubernetes环境下采用联邦学习技术复现该效果，模拟多个医院因隐私限制无法直接共享数据的场景，实现了跨机构协作的高效生成与训练。
原文摘要 · Abstract (English)
In the field of deep learning, large architectures often obtain the best performance for many tasks, but also require massive datasets. In the histological domain, tissue images are expensive to obtain and constitute sensitive medical information, raising concerns about data scarcity and privacy. Vision Transformers are state-of-the-art computer vision models that have proven helpful in many tasks, including image classification. In this work, we combine vision Transformers with generative adversarial networks to generate histopathological images related to colorectal cancer and test their quality by augmenting a training dataset, leading to improved classification accuracy. Then, we replicate this performance using the federated learning technique and a realistic Kubernetes setup with multiple nodes, simulating a scenario where the training dataset is split among several hospitals unable to share their information directly due to privacy concerns.
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